So it's twice the size of phi 3 and considerably worse? What am I missing
Another take on this: phi-3 small has 1100 ELO on LMSYS (ranked #52) while the confidence interval for Gemma 2 9B is [1170, 1200] ELO (ranked btw #15 and #25).
Gemma 2: Improving Open Language Models at a Practical Size [pdf]
41–50 of 183 posts
Re: Gemma 2: Improving Open Language Models at a Practical Size [pdf]
#42This is a great release! If you are looking to try it locally with a great interface, I am working on an app [1] and I just pushed an update to support Gemma2.
Re: Gemma 2: Improving Open Language Models at a Practical Size [pdf]
#43Hello (again) from the Gemma team! We are quite excited to push this release out and happy to answer any questions! Opinions are our own and not of Google DeepMind.
Any gemma-2-9b or 27b 4 bit GGUF's on HuggingFace yet? Thanks!
If you are still looking for it, I just made it available on an app[1] that I am working on with Gemma2 support.
Re: Gemma 2: Improving Open Language Models at a Practical Size [pdf]
#44Nice! Can you explain what you mean by "simulate training beyond the number of available tokens"?
Why does using distillation from a larger model simulate training with more tokens?
Re: Gemma 2: Improving Open Language Models at a Practical Size [pdf]
#45[deleted]
Re: Gemma 2: Improving Open Language Models at a Practical Size [pdf]
#46Hello (again) from the Gemma team! We are quite excited to push this release out and happy to answer any questions! Opinions are our own and not of Google DeepMind.
Will gemma2 be available through gemma.cpp? https://github.com/google/gemma.cpp
Re: Gemma 2: Improving Open Language Models at a Practical Size [pdf]
#47Nice! Can you explain what you mean by "simulate training beyond the number of available tokens"? Why does using distillation from a larger model simulate training with more tokens?
Hi, I work on the Gemma team (same as Alek opinions are my own).
Essentially instead of tokens that are "already there" in text, the distillation allows us to simulate training data from a larger model
Re: Gemma 2: Improving Open Language Models at a Practical Size [pdf]
#48Hello (again) from the Gemma team! We are quite excited to push this release out and happy to answer any questions! Opinions are our own and not of Google DeepMind.
Given the goal of mitigating self-proliferation risks, have you observed a decrease in the model's ability to do things like help a user setup a local LLM with local or cloud software?
How much is pre-training dataset changes, how much is tuning?
How do you think about this problem, how do you solve it?
Seems tricky to me.
Re: Gemma 2: Improving Open Language Models at a Practical Size [pdf]
#49Earlier quoted context omitted.
I wonder if Google is making Deepmind people switch from their cool original research to doing LLMs like everybody else. Having their scale in money and data, I would hire new teams of engineers who want to do LLMs and leave the Deepmind researchers do their thing. Not killing the goose that lays golden eggs.
Google is in a fight for their lives, I've fully moved over to paid services and haven't used google in about a month now.
If this were a common sentiment or rooted in reality I would imagine their stock would not be at an all time high...
Re: Gemma 2: Improving Open Language Models at a Practical Size [pdf]
#50Earlier quoted context omitted.
Any gemma-2-9b or 27b 4 bit GGUF's on HuggingFace yet? Thanks!
It's on HuggingFace already: https://huggingface.co/google/gemma-2-9b
I know the safe tensors are there, but I said GGUF 4-bit quantised, which is kinda the standard for useful local applications, a typical balanced sweet spot of performance and quality. It's makes it much easier to use, works in more places, be it personal devices or a server etc.